Quantification of subclonal selection in cancer from bulk sequencing data

Subclonal architectures are prevalent across cancer types. However, the temporal evolutionary dynamics that produce tumour subclones remain unknown. Here we measure clone dynamics in human cancers using computational modelling of subclonal selection and theoretical population genetics applied to high throughput sequencing data. Our method determines the detectable subclonal architecture of tumour samples, and simultaneously measures the selective advantage and time of appearance of each subclone. We demonstrate the accuracy of our approach and the extent to which evolutionary dynamics are recorded in the genome. Application of our method to high-depth sequencing data from breast, gastric, blood, colon and lung cancers, as well as metastatic deposits, showed that detectable subclones under selection, when present, consistently emerged early during tumour growth and had a large fitness advantage (>20%). Our quantitative framework provides new insight into the evolutionary trajectories of human cancers, facilitating predictive measurements in individual tumours from widely available sequencing data.